Explorations of using a convolutional neural network to understand brain activations during movie watching
Wonbum Sohn1,2, Xin Di1, Zhen Liang3
1Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA.
Background:
Naturalistic stimuli, such as videos, can elicit complex brain activations. However, the intricate nature of these stimuli makes it challenging to attribute specific brain functions to the resulting activations, particularly for higher-level processes such as social interactions.
Objective:
We hypothesized that activations in different layers of a convolutional neural network (VGG-16) would correspond to varying levels of brain activation, reflecting the brain's visual processing hierarchy. Additionally, we aimed to explore which brain regions would be linked to the deeper layers of the network.
Methods:
This study analyzed functional MRI data from participants watching a cartoon video. Using a pre-trained VGG-16 convolutional neural network, we mapped hierarchical features of the video to different levels of brain activation. Activation maps from various kernels and layers were extracted from video frames, and the time series of average activation patterns for each kernel were used in a voxel-wise model to examine brain responses.
Results:
Lower layers of the network were primarily associated with activations in lower visual regions, although some kernels also unexpectedly showed associations with the posterior cingulate cortex. Deeper layers were linked to more anterior and lateral regions of the visual cortex, as well as the supramarginal gyrus.
Conclusions:
This analysis demonstrated both the potential and limitations of using convolutional neural networks to connect video content with brain functions, providing valuable insights into how different brain regions respond to varying levels of visual processing.
